Seminar 6 (og 7)

SOK-2011

Author

Andrea Mannberg

På dette seminaret skal vi sammenligne tre mål på bærekraftig utvikling: - Den justerte nettosparingsraten (genuine savings, GSI) - “Social Progress Index” (SPI) alternativt “Sustainable Society Index” (SSI) som bygger på FN sine bærekraftsmål - Human Development Index - original og justert for klima og miljø, og for ulikhet.

Oppgavene består i det å først visualisere utviklingen i måleinstrumentene over tid for ulike land, og deretter diskutere fordeler og ulemper med de ulike måleinstrumentene.

Dokumentasjon:

Data:

Pakker:

suppressPackageStartupMessages(library(dplyr))
suppressPackageStartupMessages(library(plyr))
suppressPackageStartupMessages(library(ggplot2))
suppressPackageStartupMessages(library(WDI))
suppressPackageStartupMessages(library(tidyverse))
suppressPackageStartupMessages(library(countrycode))
suppressPackageStartupMessages(library(tidyr))
suppressPackageStartupMessages(library(ggrepel))
suppressPackageStartupMessages(library(spiR))
suppressPackageStartupMessages(library(readxl))
suppressPackageStartupMessages(library(readr))

Oppgave 1

Diskuter:

  • Hvorfor skal vi måle “bærekraftig utvikling”?
  • Hva ønsker vi få ut av et “godt” måleinstrument?
  • Hva kan/skal vi bruke resultatene (og måleinstrumentene) til?

Oppgave 2

Visualiser utviklingen over tid for et utvalg av land i en graf. Lagre grafen, slik at du kan ta den fram seinere

Du kan selv velge hvilke land du vil se på. Et forslag er å se på følgende land (landkode i parentes):

-   Verden (WLD)
-   USA (USA)
-   Norge (NOR)
-   China (CHN)
-   Brasilia (BRA)
-   Russland (RUS)
-   Nigeria (NGA)

GSI finns beregnet i WDI, du finner variabelnavnet her nede.

# NY.ADJ.SVNG.GN.ZS       Adjusted savings: national savings (% of GNI) - capital consumption + public expenditure on education - natural resource depletion - damage from emissions of particulate matter - damage from CO2 emissions

Oppgave 2: Løsning

df_sdg0<-WDI(
  country = c("WLD","USA", "NOR", "NGA", "BRA", "CHN", "RUS"),
  indicator = c('gsi'="NY.ADJ.SVNG.GN.ZS"),  
  start = 1999,
  end = 2019,
  extra = TRUE, 
  cache = NULL,
  latest = NULL,
  language = "en"
)

df_sdg <- subset(df_sdg0, select = c(country, iso3c, year, gsi) ) 
df_sdg <-  df_sdg %>% mutate_all(na_if,"")
df_sdg = df_sdg %>%  
  mutate(year= as.numeric(year))

p_gsi<- ggplot(data=df_sdg,aes(x=year,y=gsi, group=country, color=country)) + 
  geom_line(aes(group=country), size=1) +
  geom_point(size=2.5)+
  labs(x = "År" , y ="Justert sparerate (netto)")  +
  geom_hline(yintercept=0, size=1, color = "red")

p_gsi

Oppgave 3

\(SPI\): Gjeometrisk gjennomsnitt av tre dimensjoner:

\[ SPI = (Basic Human Needs)^{\frac{1}{3}}\cdot(FoundationsofWellbeing)^{\frac{1}{3}}\cdot(Opportunity)^{\frac{1}{3}} \]

  • Basic Human needs (gjeometrisk gjennomsnitt):
    • Nutrition and Basic medical care
    • Water and sanitation
    • Shelter
    • Personal safety
  • Foundations of wellbeing (gjeometrisk gjennomsnitt):
    • Access to basic knowledge
    • Access to information and communication
    • Health and wellness
    • Environmental quality
  • Opportunity(gjeometrisk gjennomsnitt):
    • Personal rights
    • Personal freedom and choice
    • Inclusiveness
    • Access to advanced education

\(SSI\): Tre mål:

  • Human wellbeing
    • Basic Human needs: Sufficient food, Sufficent drink, Safe sanitation
    • Personal Development and Health: Education, Healthy life, Gender equality
    • Well-balanced society: Income distribution, Population growth, Good governance

\[ SSI_{HB} = (Basic Human Needs)^{\frac{1}{3}}\cdot(Personal Development And Health)^{\frac{1}{3}}\cdot(Wellbalanced society)^{\frac{1}{3}} \]

  • Environmental wellbeing
    • Natural resources: Biodiversity, Renewable water resources, consumption
    • Climate and Energy: Energy use, Grennhouse gases, Renewable energy

\[ SSI_{NB} = (Natural Resources)^{\frac{1}{2}}\cdot(Climate And Energy)^{\frac{1}{2}} \]

  • Economic wellbeing
    • Transition: Organic farming, Genuine savings
    • Economy: GDP, Employment, Public debt

\[ SSI_{EB} = (Transition)^{\frac{1}{2}}\cdot(Economy)^{\frac{1}{2}} \]

Sustainable Society Index (SSI)

Oppgave 3

  • Visualiser utviklingen i SPI over tid for samme utvalg av land som du brukte til oppgave 1 (i spiR er koden for verden “WWW”). Bruk de Årene som er tilgjengelige.

  • Lagre grafen, slik at du kan ta den fram seinere

Du laster inn data fra spiR ved bruk av koden her nede:

df_spir<- spir_data(country = c("WWW", "USA", "NOR", "NGA", "CHN", "BRA", "RUS"), indicators="SPI", )

SPI er allerede beregnet i datasettet. Du kan se de indikatorer som inngC%r i SPI ved C% bruke koden her nede:

myIndicator <- spir_indicator()

Oppgave 3: Løsning spiR

df_spir<- spir_data(country = c("WWW", "USA", "NOR", "NGA", "CHN", "BRA", "RUS"), indicators="SPI", )

df_spir = df_spir %>%  
  mutate(year = as.numeric(year))

p_spi<- ggplot(data=df_spir,aes(x=year,y=value, group=country_name, color=country_name)) + 
  geom_line(aes(group=country_name), size=1) +
  geom_point(size=2.5)+
  labs(x = "År" , y ="Social Progress Index")  +
  theme(legend.position="none")+
  geom_label_repel(
    data=df_spir %>% group_by(country_name) %>%
      filter(year==max(year)),
    aes(year, value, fill = factor(value), label = sprintf('%6.2g', value)),
    color = "black",
    fill = "white")+
  geom_label_repel(
    data=df_spir %>% group_by(country_name) %>%
      filter(year==min(year)),
    aes(year, value, fill = factor(value), label = sprintf('%6.2g', value)),
    color = "black",
    fill = "white")+
  scale_x_continuous(breaks=c(2010, 2012, 2014, 2016, 2018, 2020 ))+
  theme_classic()

p_spi

myIndicator <- spir_indicator()
myIndicator$indicator_name
 [1] "Social Progress Index"                                                                 
 [2] "Basic Human Needs"                                                                     
 [3] "Foundations of Wellbeing"                                                              
 [4] "Opportunity"                                                                           
 [5] "Nutrition and Basic Medical Care"                                                      
 [6] "Water and Sanitation"                                                                  
 [7] "Shelter"                                                                               
 [8] "Personal Safety"                                                                       
 [9] "Access to Basic Knowledge"                                                             
[10] "Access to Information and Communications"                                              
[11] "Health and Wellness"                                                                   
[12] "Environmental Quality"                                                                 
[13] "Personal Rights"                                                                       
[14] "Personal Freedom and Choice"                                                           
[15] "Inclusiveness"                                                                         
[16] "Access to Advanced Education"                                                          
[17] "Undernourishment (% of pop.)"                                                          
[18] "Maternal mortality rate (deaths/100,000 live births)"                                  
[19] "Child mortality rate (deaths/1,000 live births)"                                       
[20] "Child stunting (% of children)"                                                        
[21] "Deaths from infectious diseases (deaths/100,000 people)"                               
[22] "Access to at least basic drinking water (% of pop.)"                                   
[23] "Access to piped water (% of pop.)"                                                     
[24] "Access to at least basic sanitation facilities (% of pop.)"                            
[25] "Rural open defecation (% of pop.)"                                                     
[26] "Access to electricity (% of pop.)"                                                     
[27] "Quality of electricity supply (1=low; 7=high)"                                         
[28] "Household air pollution attributable deaths (deaths/100,000 people)"                   
[29] "Access to clean fuels and technology for cooking (% of pop.)"                          
[30] "Homicide rate (deaths/100,000 people)"                                                 
[31] "Perceived criminality (1=low; 5=high)"                                                 
[32] "Political killings and torture (0=low freedom; 1=high freedom)"                        
[33] "Traffic deaths (deaths/100,000 people)"                                                
[34] "Adult literacy rate (% of pop. aged 15+)"                                              
[35] "Primary school enrollment (% of children)"                                             
[36] "Secondary school enrollment (% of children)"                                           
[37] "Gender parity in secondary enrollment (distance from parity)"                          
[38] "Access to quality education (0=unequal; 4=equal)"                                      
[39] "Mobile telephone subscriptions (subscriptions/100 people)"                             
[40] "Internet users (% of pop.)"                                                            
[41] "Access to online governance (0=low; 1=high)"                                           
[42] "Media censorship (0=frequent; 4=rare)"                                                 
[43] "Life expectancy at 60 (years)"                                                         
[44] "Premature deaths from non-communicable diseases (deaths/100,000 people)"               
[45] "Access to essential services(0=none; 100=full coverage)"                               
[46] "Access to quality healthcare (0=unequal; 4=equal)"                                     
[47] "Outdoor air pollution attributable deaths (deaths/100,000 people)"                     
[48] "Greenhouse gas emissions (CO2 equivalents/GDP)"                                        
[49] "Biome protection"                                                                      
[50] "Political rights (0=no rights; 40=full rights)"                                        
[51] "Freedom of expression (0=no freedom; 1=full freedom)"                                  
[52] "Freedom of religion (0=no freedom; 4=full freedom)"                                    
[53] "Access to justice (0=non-existent; 1=observed)"                                        
[54] "Property rights for women (0=no rights; 5=full rights)"                                
[55] "Vulnerable employment (% of employees)"                                                
[56] "Early marriage (% of women)"                                                           
[57] "Satisfied demand for contraception (% of women)"                                       
[58] "Corruption (0=high; 100=low)"                                                          
[59] "Acceptance of gays and lesbians (0=low; 100=high)"                                     
[60] "Discrimination and violence against minorities (1=low; 10=high)"                       
[61] "Equality of political power by gender (0=unequal power; 4=equal power)"                
[62] "Equality of political power by socioeconomic position (0=unequal power; 4=equal power)"
[63] "Equality of political power by social group (0=unequal power; 4=equal power)"          
[64] "Years of tertiary schooling"                                                           
[65] "Womens average years in school"                                                        
[66] "Globally ranked universities (points)"                                                 
[67] "Percent of tertiary students enrolled in globally ranked universities"                 

Oppgave 3: Løsning SSI

Du finner originaldata til oppgaven her: TS Köln

df_ssi <- read_excel("C:/Users/ama123/OneDrive - UiT Office 365/Data/Undervisning/sok2011/data/ssi_2010_2016.xlsx")
df_ssi_sub <- df_ssi  %>% arrange(country, year) %>%
  filter(country=="United States"|country=="Norway"|country=="China" |country=="Russia"|country=="Brazil"|country=="Nigeria")


p_ssi_hwb <- ggplot(data=df_ssi_sub,aes(x=year,y=hwb, group=country, color=country)) + 
  geom_line(aes(group=country), size=1) +
  geom_point(size=2.5)+
  labs(x = "År" , y ="SSI - Human wellbeing")  +
  theme(legend.position="none")+
  geom_label_repel(
    data=df_ssi_sub %>% group_by(country) %>%
      filter(year==max(year)),
    aes(year, hwb, fill = factor(hwb), label = sprintf('%6.2g', hwb)),
    color = "black",
    fill = "white")+
  geom_label_repel(
    data=df_ssi_sub %>% group_by(country) %>%
      filter(year==min(year)),
    aes(year, hwb, fill = factor(hwb), label = sprintf('%6.2g', hwb)),
    color = "black",
    fill = "white")+
  scale_x_continuous(breaks=c( 2006, 2008, 2010, 2012, 2014, 2016))+
  theme_classic()

p_ssi_nwb <- ggplot(data=df_ssi_sub,aes(x=year,y=nwb, group=country, color=country)) + 
  geom_line(aes(group=country), size=1) +
  geom_point(size=2.5)+
  labs(x = "År" , y ="SSI - Environmental wellbeing")  +
  theme(legend.position="none")+
  geom_label_repel(
    data=df_ssi_sub %>% group_by(country) %>%
      filter(year==max(year)),
    aes(year, nwb, fill = factor(nwb), label = sprintf('%6.2g', nwb)),
    color = "black",
    fill = "white")+
  geom_label_repel(
    data=df_ssi_sub %>% group_by(country) %>%
      filter(year==min(year)),
    aes(year, nwb, fill = factor(nwb), label = sprintf('%6.2g', nwb)),
    color = "black",
    fill = "white")+
  scale_x_continuous(breaks=c( 2006, 2008, 2010, 2012, 2014, 2016))+
  theme_classic()

p_ssi_ewb <- ggplot(data=df_ssi_sub,aes(x=year,y=ewb, group=country, color=country)) + 
  geom_line(aes(group=country), size=1) +
  geom_point(size=2.5)+
  labs(x = "År" , y ="SSI - Economic wellbeing")  +
  theme(legend.position="none")+
  geom_label_repel(
    data=df_ssi_sub %>% group_by(country) %>%
      filter(year==max(year)),
    aes(year, ewb, fill = factor(ewb), label = sprintf('%6.2g', ewb)),
    color = "black",
    fill = "white")+
  geom_label_repel(
    data=df_ssi_sub %>% group_by(country) %>%
      filter(year==min(year)),
    aes(year, ewb, fill = factor(ewb), label = sprintf('%6.2g', ewb)),
    color = "black",
    fill = "white")+
  scale_x_continuous(breaks=c( 2006, 2008, 2010, 2012, 2014, 2016))+
  theme_classic()

p_ssi_nwb

p_ssi_hwb

p_ssi_ewb

Oppgave 4 - PHDI og IHDI

Data til PHDI og IHDI kommer fra UNDP.

\[ PHDI = HDI\cdot\frac{(CO_{2,Index} + MaterialFootprint_{Index})}{2} \] \[ IHDI = HDI\cdot \frac{(IneqLifeExp_{Index} + IneqEducation_{Index}+ IneqIncome_{Index})}{3} \]

  • Dere finner en beskrivelse av HDI justert for klima og miljø her:
  • Dere finner en beskrivelse av HDI justert for ulikhet her:

Tverrsnittsdata:

Variabler

# datasett phdi
# hdi_rank: human development index - country rank
# hdi: human development index
# phdi: planetary pressures adjusted hdi
# diff_phdi: difference phid hdi in percent
# diff_hdi_rank: difference in country rank phid hdi
# p_adj: adjustment factor for planetary pressures
# co_ton: CO2 emissions (tonnes)
# co_index: CO2 emissions index
# mfot_perc: material footprint (tonnes)
# mfoot_index: material footprint index

#datasett ihdi
# ihdi: inequality adjusted hdi
# diff_ihdi: difference ihid hdi in percent
# diff_idi_rank: difference in country rank ihid hdi
# i_adj: adjustment factor for inequality
# i_life_perc: Inequality in life expectancy (%)
# i_life_index: Inequality in life expectancy index
# i_educ_perc: Inequality in education (%)
# i_educ_index: Inequality in education index
# i_inc_perc: Inequality in income (%)
# i_inc_index: Inequality in income index
# inc_40: Income share held by poorest 40% 2010-2021
# Inc_10: Income share held by richest 10% 2010-2021
# inc_1: Income share held by riches 1% 2010-2021
# gini: Gini coefficient 2010-2021

Paneldata

# hdi: human development index
# phdi: planetary pressures adjusted hdi
# ihdi: inequality adjusted hdi

Oppgave 4 - PHDI og IHDI

Visualiser relasjonen mellom 1) HDI og HDI justert for klima og miljø, og 2) HDI og HDI justert for ulikhet, (+ evt relasjonen mellom de to justerte målene)

Data-hjelp (tverrsnitt):

phdi <- read_excel("C:/Users/ama123/OneDrive - UiT Office 365/Data/Undervisning/sok2011/data/HDI_undp/phdi.xlsx")
ihdi <- read_excel("C:/Users/ama123/OneDrive - UiT Office 365/Data/Undervisning/sok2011/data/HDI_undp/ihdi.xlsx")

phdi <-  phdi %>% mutate_all(na_if,"..")
phdi$iso3c<-countryname(phdi$country, destination = 'iso3c') # Legg til kode til alle land (kan brukes til grafisk framstilling)

phdi = phdi %>%  
  mutate(hdi = as.numeric(hdi)) %>% 
  mutate(phdi = as.numeric(phdi))%>% 
  mutate(diff_phdi = as.numeric(diff_phdi))

ihdi <-  ihdi %>% mutate_all(na_if,"..")
ihdi$iso3c<-countryname(ihdi$country, destination = 'iso3c')


ihdi = ihdi %>%  
  mutate(hdi = as.numeric(hdi)) %>% 
  mutate(ihdi = as.numeric(ihdi))%>% 
  mutate(diff_ihdi = as.numeric(diff_ihdi))

df_pihdi <- left_join(phdi, ihdi, by=c('iso3c','country', 'hdi', 'hdi_rank', 'dev_level')) #Sett sammen de to datasettene, slik at vi kan sammenligne dem

df_pihdi$pihdi<-df_pihdi$hdi - ((df_pihdi$diff_phdi)/100)*(df_pihdi$hdi) - ((df_pihdi$diff_ihdi)/100)*(df_pihdi$hdi) # lage et instrument som korrigerer bC%de for ulikhet og miljC8.

Data-hjelp (Paneldata):

Data-materialet er organisert i “wide-format” (som et tverrsnitt) og inneholder en stor mengde variabler. Vi ønsker å endre format på data slik at vi får en variabel som måler “År” og koble våre observasjoner på HDI, PHDI og IHDI til dette.

hdi_orig <- read_csv("C:/Users/ama123/OneDrive - UiT Office 365/Data/Undervisning/sok2011/data/HDI_undp/HDR21-22_Composite_indices_complete_time_series.csv")

# 1. Justere data knyttet til HDI
df_hdi <- hdi_orig %>%
  select(iso3, country, starts_with("hdi")) %>% # Velg ut relevante variabler
  select(-c(hdi_rank_2021, hdicode, starts_with("hdi_f"), starts_with("hdi_m"))) %>% # Ta vekk variabler som har med "hdi" i navnet, men som ikke skal brukes. 
  pivot_longer(             # Endre format fra wide (tverrsnitt) til long (panel)
    cols=-c(iso3, country), 
    names_sep = "_",
    names_to = c(NA, "year"), 
    values_to = "hdi") %>%
mutate(year = as.numeric(year))

# Samme prosedyre for C% lage et panel for phdi
df_phdi <- hdi_orig %>%
  select(iso3, country, starts_with("phdi")) %>%
  pivot_longer(
    cols=-c(iso3, country),
    names_sep = "_",
    names_to = c(NA, "year"), 
    values_to = "phdi") %>%
  mutate(year = as.numeric(year))

# Samme prosedyre for C% lage et panel for ihdi
df_ihdi <- hdi_orig %>%
  select(iso3, country, starts_with("ihdi")) %>%
  pivot_longer(
    cols=-c(iso3, country),
    names_sep = "_",
    names_to = c(NA, "year"), 
    values_to = "ihdi") %>%
  mutate(year = as.numeric(year))


# velg ut land, og koble data til hdi data (dersom du C8nsker C% sammenligne hdi og de to justerte mC%lene)
df_phdi_sub <- df_phdi  %>% 
  left_join(df_hdi, df_phdi, by=c("country", "iso3", "year")) %>% 
  filter(iso3=="ZZK.WORLD" |iso3=="USA"|iso3=="NOR"|iso3=="CHN" |iso3=="RUS"|iso3=="BRA"|iso3=="NGA")


df_ihdi_sub <- df_ihdi  %>% 
  left_join(df_hdi, df_ihdi, by=c("country", "iso3", "year")) %>% 
  filter(year>=2010) %>% 
  filter(iso3=="ZZK.WORLD" |iso3=="USA"|iso3=="NOR"|iso3=="CHN" |iso3=="RUS"|iso3=="BRA"|iso3=="NGA")

Oppgave 4: Løsning

Paneldata

p_ihdi_t<- ggplot(data=df_ihdi_sub,aes(x=year,y=ihdi, group=country, color=country)) + 
  geom_line(aes(group=country), size=1) +
  geom_point(size=2.5)+
  labs(x = "År" , y ="HDI adjusted for inequality")  +
  theme(legend.position="none")+
  geom_label_repel(
    data=df_ihdi_sub %>% group_by(country) %>%
      filter(year==max(year)),
    aes(year, ihdi, fill = factor(ihdi), label = sprintf('%6.2g', ihdi)),
    color = "black",
    fill = "white")+
  geom_label_repel(
    data=df_ihdi_sub %>% group_by(country) %>%
      filter(year==min(year)),
    aes(year, ihdi, fill = factor(ihdi), label = sprintf('%6.2g', ihdi)),
    color = "black",
    fill = "white")+
  scale_x_continuous(breaks=c(2010, 2012, 2014, 2016, 2018, 2020))+
  theme_classic()

p_ihdi_t

p_phdi_t<- ggplot(data=df_phdi_sub,aes(x=year,y=phdi, group=country, color=country)) + 
  geom_line(aes(group=country), size=1) +
  geom_point(size=2.5)+
  labs(x = "År" , y ="HDI adjusted for planetary pressures")  +
  theme(legend.position="none")+
  geom_label_repel(
    data=df_phdi_sub %>% group_by(country) %>%
      filter(year==max(year)),
    aes(year, phdi, fill = factor(ihdi), label = sprintf('%6.2g', phdi)),
    color = "black",
    fill = "white")+
  geom_label_repel(
    data=df_phdi_sub %>% group_by(country) %>%
      filter(year==min(year)),
    aes(year, phdi, fill = factor(phdi), label = sprintf('%6.2g', phdi)),
    color = "black",
    fill = "white")+
  scale_x_continuous(breaks=c(1990, 1995, 2000, 2005, 2010, 2015, 2020))+
  theme_classic()

p_phdi_t

Tverrsnitsdata (sammenligning HDI og justerte måleinstrumenter

p_phdi <- phdi %>%
  ggplot(aes(x = hdi, y = phdi)) +
  ylab("HDI adjusted for planetary pressures") + 
  xlab("Human development index") + 
  geom_text(aes(label=iso3c),size=3)+
  theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(), panel.background = element_rect(fill = 'white'))+
  scale_y_continuous(breaks=c(0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0))+
  scale_x_continuous(breaks=c(0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0))+
  geom_abline(intercept = 0, slope = 1, size = 1, color="red") +
  theme_classic()


p_d_phdi<- phdi %>%
  ggplot(aes(x = hdi, y = diff_phdi)) +
  ylab("% adjustment for planetary pressure") + 
  xlab("Human development index") + 
  geom_text(aes(label=iso3c),size=3)+
  theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(), panel.background = element_rect(fill = 'white'))+
  scale_x_continuous(breaks=c(0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0))+
  theme_classic()

p_ihdi<- ihdi %>%
  ggplot(aes(x = hdi, y = ihdi)) +
  ylab("HDI adjusted for inequality") + 
  xlab("Human development index") + 
  geom_text(aes(label=iso3c),size=3)+
  theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(), panel.background = element_rect(fill = 'white'))+
  scale_y_continuous(breaks=c(0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0))+
  scale_x_continuous(breaks=c(0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0))+
  geom_abline(intercept = 0, slope = 1, size = 1, color="red") +
  expand_limits(x = 0.2, y = 0.2) +
  theme_classic()


p_d_ihdi<- ihdi %>%
  ggplot(aes(x = hdi, y = diff_ihdi)) +
  ylab("% adjustment for inequality") + 
  xlab("Human development index") + 
  geom_text(aes(label=iso3c),size=3)+
  theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(), panel.background = element_rect(fill = 'white'))+
  scale_x_continuous(breaks=c(0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0))+
  expand_limits(x = 0.2, y = 0.2) +
  theme_classic()



p_pihdi<- df_pihdi %>%
  ggplot(aes(x = hdi, y = pihdi)) +
  ylab("HDI adjusted for planetary pressures, and inequality") + 
  xlab("Human development index") + 
  geom_text(aes(label=iso3c),size=3)+
  theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(), panel.background = element_rect(fill = 'white'))+
 scale_y_continuous(breaks=c(0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0))+
  scale_x_continuous(breaks=c(0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0))+
  geom_abline(intercept = 0, slope = 1, size = 1, color="red") +
  theme_classic()


p_phdi_ihdi<- df_pihdi %>%
  ggplot(aes(x = ihdi, y = phdi)) +
  ylab("HDI adjusted for planetary pressures") + 
  xlab("HDI adjusted for inequality") + 
  geom_text(aes(label=iso3c),size=3)+
  theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(), panel.background = element_rect(fill = 'white'))+
  scale_y_continuous(breaks=c(0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0))+
  scale_x_continuous(breaks=c(0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0))+
  geom_abline(intercept = 0, slope = 1, size = 1, color="red") +
  expand_limits(x = 0.2, y = 0.2) +
  theme_classic()

p_phdi

p_ihdi

p_d_phdi

p_d_ihdi

p_pihdi

p_phdi_ihdi

Oppgave 5

Sammenlign resultatene fra din grafiske analyse.

  • Hvordan skal vi tolke de enkelte grafene?
  • Hvilket av måleinstrumentene syns du er “best” og hvorfor? Diskuter fordeler og ulemper med å evaluere bærkraftig utvikling ved bruk av de ulike måleinstrumentene.
p_gsi

p_spi

Sustainable society index

SSI

p_ssi_hwb

p_ssi_nwb

p_ssi_ewb

HDI

p_phdi_t

p_ihdi_t

p_phdi

p_ihdi

p_d_phdi

p_d_ihdi

p_pihdi

p_phdi_ihdi